Popular GitHub Repositories for Microcontroller

TinyML AI inference library

A higher-level Neural Network library for microcontrollers.

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory

Machine Learning inference engine for Microcontrollers and Embedded devices

Open Neural Network Exchange to C compiler.

Neural Networks with low bit weights on low end 32 bit microcontrollers such as the CH32V003 RISC-V Microcontroller and others

Be Driven ?

SensiML's open-source AutoML solution for Edge AI model development

A simple face recognition using ESP32 Microcontroller with camera, and leveraging cloud-based inference powered by Azure Custom Vision.

stm32-speech-recognition-and-traduction is a project developed for the Advances in Operating Systems exam at the University of Milan (academic year 2020-2021). It implements a speech recognition and speech-to-text translation system using a pre-trained machine learning model running on the stm32f407vg microcontroller.

⚙️ A comprehensive Tutorials on ATMEL 8051 Series Microcontrollers (AT89S51/52) Programming using Assembly Language. Learn how to use 8051 Microcontrollers from the ground up! This repository covers everything from the basics to advanced interfacing techniques with examples. Using Proteus for simulations, you'll get hands-on experiences. Enjoy!

Code for IEEE Internet Computing Journal paper 'OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices'

TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning

Intelligent Robotic Arm is a 5 dof autonomous arm with webcam attached to it for vision.

A tool to support using classification models in low-power and microcontroller-based embedded systems.

Supplementary material for IEEE Services Computing paper 'An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware'

MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).

Compress, Deploy and Inference YOLOv7 on low-cost MCUs

Example in C that uses TensorFlow Lite for Microcontrollers to run a simple CNN model for image classification on an embedded system.

Building a security camera with ESP32-CAM and workers-ai for object detection